Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

Automatic segmentation of spinal cord injury using coarse-to-fine strategy

Authors
Jiali Cui, Yubo Ma, Wei Xie, Yiding Wang, Ruiming Jia
Corresponding Author
Jiali Cui
Available Online November 2016.
DOI
https://doi.org/10.2991/aest-16.2016.16How to use a DOI?
Keywords
medical image segmentation; automatic segmentation; spinal cord injury; coarse-to-fine strategy.
Abstract
Medical image segmentation is a key issue in the field of medical image processing and analysis. Segmentation of spinal cord injury draws interests of researchers recently. To eliminate human computer interaction in segmentation of spinal cord injury, an automatic segmentation of spinal cord injury is proposed. A self-adaptive non-edge information smoothing algorithm was presented to realize filtering processing of spinal cord injury (SCI) images. The first time to use Deformable Parts Model (DPM) algorithm on the positioning detection of spine in an SCI image. A large number of non-scar information noises were removed and the detection range of scars was further reduced. Based on Coarse-to-fine strategy, a fully-automated spinal cord scar injury segmentation algorithm is put forward. The experimental results demonstrated the effectiveness of this algorithm.
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Proceedings
2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-257-2
DOI
https://doi.org/10.2991/aest-16.2016.16How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jiali Cui
AU  - Yubo Ma
AU  - Wei Xie
AU  - Yiding Wang
AU  - Ruiming Jia
PY  - 2016/11
DA  - 2016/11
TI  - Automatic segmentation of spinal cord injury using coarse-to-fine strategy
BT  - 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/aest-16.2016.16
DO  - https://doi.org/10.2991/aest-16.2016.16
ID  - Cui2016/11
ER  -